ADDIS: Aggregate Data Drug Information System for drug...
Transcript of ADDIS: Aggregate Data Drug Information System for drug...
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
ADDIS: Aggregate Data Drug Information Systemfor drug benefit-risk analysis and automated
evidence synthesis
Tommi Tervonen
Faculty of Economics and Business, University of Groningen, The Netherlands
Presentation @ Universidade Federal de Santa Catarina11 June 2010
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Introduction: evidence-based medicine (EBM)
Evidence-based medicine aims to apply the best availableevidence gained from scientific research to medical decisionmaking
A large share of decisions made by health care professionalsare informed by evidence-based medicine, e.g. prescription,regulatory- and reimbursement policy decisions
Although the scientific evidence is transparent and achievedwith methodological rigour, the actual decisions are oftenunstructured, ad hoc and lack transparency as the treatmentbenefit-risk valuation is not explicit
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Introduction: application of EBM in drug benefit-riskanalysis
For a drug to be granted marketing authorization, it must beproven efficant, safe, and have a sufficient benefit-risk (BR)profile compared to other drugs already in the market
Eichler & al., Nature Drug Disc, 2008
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Project Escher
Escher is a national research project of the Dutch TopInstitute Pharma that aims to improve drug regulationthrough science
16 PhD students and 4 PostDocs working in 5 universities(RUG/UMCG, UU/UMCU, Erasmus MC) in collaborationwith the industry (MSD, GSK, Amgen, WINap)
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Project Escher
Escher is a national research project of the Dutch TopInstitute Pharma that aims to improve drug regulationthrough science
16 PhD students and 4 PostDocs working in 5 universities(RUG/UMCG, UU/UMCU, Erasmus MC) in collaborationwith the industry (MSD, GSK, Amgen, WINap)
Work package 3.2 (RUG/UMCG withSchering-Plough/Merck) aims to bridge the gap betweenaggregate clinical data and evidence-based drug regulation byhaving useful methods for benefit-risk analysis implemented inusable software (which would then be used in real-life decisionmaking)
Useful/Usable/Used: Keen & Sol, IOS Press, 2008
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Drug benefit-risk analysis
BR analysis should includeall relevant evidence, andtherefore apply (network)meta-analysis
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Drug benefit-risk analysis
BR analysis should includeall relevant evidence, andtherefore apply (network)meta-analysis
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Drug benefit-risk analysis
Problems
1 Inclusion of all relevantevidence in themeta-analysis is notguaranteed
2 The BR analysis isunstructured andnon-transparent
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Drug benefit-risk analysis
Problems
1 Inclusion of all relevantevidence in themeta-analysis is notguaranteed
2 The BR analysis isunstructured andnon-transparent
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Case study of BR analysis
Hansen & al. (Ann Intern Med, 2005) assessed safety andefficacy of four second generation antidepressants andconcluded that there are “no significant differences among thedrugs”
In general, the assessment of antidepressants is hard; placeboeffect is always present causing high uncertainty on the results
Q’s:1 How can the benefit-risk assessment of second-generation
antidepressant be structured based on evidence from theclinical trials?
2 Can we come up with something better than “no significantdifferences”?
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Case study: data from meta-analysis
Hansen & al., Ann Intern Med, 2005
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Case study: data from meta-analysis
Hansen & al., Ann Intern Med, 2005
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Case study: data from meta-analysis
Hansen & al., Ann Intern Med, 2005
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Case study: data from meta-analysis
Hansen & al., Ann Intern Med, 2005
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Approach
Separate clinical data (measurements) from the valuejudgements (MCDA)
Include all data present in the original analysis (imprecisemeasurements)
Provide metrics for decision uncertainty
Enable model generation for re-applicability
We chose to apply Stochastic Multicriteria AcceptabilityAnalysis (SMAA)
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Approach
Separate clinical data (measurements) from the valuejudgements (MCDA)
Include all data present in the original analysis (imprecisemeasurements)
Provide metrics for decision uncertainty
Enable model generation for re-applicability
We chose to apply Stochastic Multicriteria AcceptabilityAnalysis (SMAA)
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
SMAA(-2)
SMAA(-2) is a multi-criteria decision aiding (MCDA) methodfor ranking a set of alternatives evaluated on basis of a set ofcriteria
Preference information expressed with a weight vector and avalue function of a commonly accepted shape (in practiceoften linear one)
Uncertain or imprecise criteria values are represented bystochastic variables that map the deterministic value functionsto value distributions
Lahdelma & Salminen, EJOR, 1998 / Tervonen & Figueira, JMCDA, 2008
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Traditional MCDA
Lahdelma & Salminen, Springer, 2010
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Inverse approach
Lahdelma & Salminen, Springer, 2010
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Weight space
Total lack of preferenceinformation is represented bya uniform joint probabilitydistribution of the weightspace
If some preferenceinformation is available, theweight space can berestricted with linearconstraints
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Rank acceptability index
Describes the share of different weights and criteria measurementsranking an alternative on a certain rank
bri =
∫ξ∈χ
fχ(ξ)
∫w∈W r
i (ξ)fW (w) dw dξ
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Central weight vector & confidence factor
Central weight vector describes the preferences of a typicalDM supporting this alternative with the assumed preferencemodel
CW’s are used for inverse approach: instead of askingpreferences and giving results, answers the question “whichpreferences support an alternative to be the most preferredone?”
Confidence factor is the probability for an alternative to bethe preferred one with the preferences expressed by its centralweight vector
CF measures whether the criteria measurements are accurateenough to discern the efficient alternatives
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Computation
Analytical techniques based on discretizing the integrals withrespect to each dimension are infeasible, so the integrals areestimated through Monte Carlo simulation
10000 simulations provide sufficient accuracy for the indices
Algorithm has less-than squared mean complexity and is veryfast in practice
Tervonen & Lahdelma, EJOR, 2007
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
MCDA Model Generation
Tervonen, URPDM’2010
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
When cannot the MCDA-BR-model be generated?
Paroxetine
Bupropion
(1)
Duloxetine
(1)
Mirtazapine
(2)
Venlafaxine
(2)
Sertraline
(3)
(1)
Escitalopram
(2)
Fluoxetine
(8)
(2)
(1)
(1)
(7)
Fluvoxamine
(2)
(6)
Citalopram
(1)
(3) (1) (2)
(1) (2)
Figure: Evidence network of studies comparing efficacy of 2nd genantidepressants
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Meta-analysis limitations
Fluoxetine
Paroxetine
(8)
Sertraline
(5)
Venlafaxine
(6)
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Meta-analysis limitations
Paroxetine
Bupropion
(1)
Duloxetine
(1)
Mirtazapine
(2)
Venlafaxine
(2)
Sertraline
(3)
(1)
Escitalopram
(2)
Fluoxetine
(8)
(2)
(1)
(1)
(7)
Fluvoxamine
(2)
(6)
Citalopram
(1)
(3) (1) (2)
(1) (2)
Figure: Evidence network of studies comparing efficacy of 2nd genantidepressants
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Meta-analysis limitations
Paroxetine
Bupropion
(1)
Duloxetine
(1)
Mirtazapine
(2)
Venlafaxine
(2)
Sertraline
(3)
(1)
Escitalopram
(2)
Fluoxetine
(8)
(2)
(1)
(1)
(7)
Fluvoxamine
(2)
(6)
Citalopram
(1)
(3) (1) (2)
(1) (2)
Uncertainty about fluoxetine not represented explicitly
What happens if we choose another baseline?
Other studies included → possibly different results
Not all drugs can be included (escitalopram)
We’re “double counting” multi-arm trials
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Solution: apply network meta-analysis
Paroxetine
Bupropion
(1)
Duloxetine
(1)
Mirtazapine
(2)
Venlafaxine
(2)
Sertraline
(3)
(1)
Escitalopram
(2)
Fluoxetine
(8)
(2)
(1)
(1)
(7)
Fluvoxamine
(2)
(6)
Citalopram
(1)
(3) (1) (2)
(1) (2)
Include all evidence in one mixed-treatment comparison(MTC) analysis
Van Valkenhoef & al., manuscript, 2010
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Network meta-analysis problems
Paroxetine
Bupropion
(1)
Duloxetine
(1)
Mirtazapine
(2)
Venlafaxine
(2)
Sertraline
(3)
(1)
Escitalopram
(2)
Fluoxetine
(8)
(2)
(1)
(1)
(7)
Fluvoxamine
(2)
(6)
Citalopram
(1)
(3) (1) (2)
(1) (2)
Model considerably more complex (Bayesian instead ofregression)
Treatment network inconsistency must be evaluated
No algorithms for generating MTC models exist(ed)
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
MTC/SMAA application
Zhao, MSc thesis @ KTH.se, 2010 (to appear)
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Clinical trial data models
Previous advances in clinical trial informatics have aimed for acomprehensive data model
Comprehensiveness is conflicting with usefulness
We provide a minimal data model to enable useful analyses
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Minimal data model
*
*
1
*
name: Stringdirection: Enum
OutcomeMeasure *1 sampleSize:
Integer
Measurement
name:StringStudy
name: Stringcode: Long
Indication
*
1
1 2 .. *size: Integer
Arm
name: StringatcCode: String
Drug
*
1
Dosequantity: Doubleunit: Enum
1 1
1 .. *
1
{One measurement for every arm-
OutcomeMeasure pair}
rate: IntegerRateMeasurement
mean: DoublestdDev: Double
ContinuousMeasurement
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Aggregate Data Drug Information System
Import & store trial design & results
Generation & computation of1 meta-analyses2 network meta-analyses3 SMAA benefit-risk models
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Time for live demo!
ADDIS v0.9-SNAPSHOT from www.drugis.org
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Conclusions
Drug benefit-risk analysis can be structured with multi-criteriadecision analysis (MCDA)
The MCDA models can take into account all relevant clinicalevidence in their original format by applying SMAA+MTC
Minimal data model is required for generation of usefulanalysis models
The ADDIS software is open source, and implements alreadyall this!
Obrigado pela sua atencao!
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Conclusions
Drug benefit-risk analysis can be structured with multi-criteriadecision analysis (MCDA)
The MCDA models can take into account all relevant clinicalevidence in their original format by applying SMAA+MTC
Minimal data model is required for generation of usefulanalysis models
The ADDIS software is open source, and implements alreadyall this!
Obrigado pela sua atencao!
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Conclusions
Drug benefit-risk analysis can be structured with multi-criteriadecision analysis (MCDA)
The MCDA models can take into account all relevant clinicalevidence in their original format by applying SMAA+MTC
Minimal data model is required for generation of usefulanalysis models
The ADDIS software is open source, and implements alreadyall this!
Obrigado pela sua atencao!
Introduction SMAA-2 Model generation Data model ADDIS Conclusions
Conclusions
Drug benefit-risk analysis can be structured with multi-criteriadecision analysis (MCDA)
The MCDA models can take into account all relevant clinicalevidence in their original format by applying SMAA+MTC
Minimal data model is required for generation of usefulanalysis models
The ADDIS software is open source, and implements alreadyall this!
Obrigado pela sua atencao!